Time-sensitive
JOB TO BE DONESend a package fast with minimal decision effort.
FRICTIONDelivery time is hard to compare across apps.
HomeHelping senders compare delivery services by price, ETA and service type, without leaving the flow or relying on memory.

Senders already know what matters to them: price, delivery time and service type. What they do not know is which courier is the right choice.
To compare, they leave the flow, enter the same pickup and drop-off details again and hold the differences in memory.
Send a package fast with minimal decision effort.
FRICTIONDelivery time is hard to compare across apps.
Find the most affordable option without repeating inputs.
FRICTIONPrices are scattered and mental comparison is unreliable.
Make quick, confident decisions based on prior experience.
FRICTIONEvery comparison starts from scratch.
AloPeyk · SnappBox · Tapsi
Reviewed how price, time and service type are surfaced in similar products.
A reference point makes similar options easier to judge.
Best-option emphasis reduces cognitive load and speeds decisions.
Show all options first, then let advanced users refine.
A combined default is more useful than a single-variable ranking.
From landing on comparison to selecting a service
Faster decisions → lower drop-offFilter, sort and criteria engagement
Clearer differences → better retentionUsers leaving before service selection
Lower exits → higher conversionChoose the service that fits your priorities
The two strongest decision signals stay in the same scan line.
A baseline guides the user without hiding alternative options.
Advanced filters remain available without making the first view dense.
Frequent users performed better than new users, revealing the value of recognition, history and guided comparison.
Suggest the best-fit service using a sender's priorities and decision patterns, while keeping the reasoning visible.
Transparent comparison and a partner discount.
A useful decision layer instead of another dead-end list.
Commission-based partnerships without deceptive patterns.
The next step is broader user testing, edge-case validation and an explainable AI layer that supports, not replaces, the user's decision.